Messaging system with circumstance configuration framework for hardware

ABSTRACT

An example method comprises: receiving, at a server from a first client device, a request for access to a client feature on the first client device; determining, by the server, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the server, device capabilities needed for the determined rule; determining, by the server, nodes that can be executed and nodes that cannot be executed, based on the device capabilities, the nodes that can be executed including device hardware capabilities and the nodes that cannot be executed including real-time device capabilities; executing, by the server nodes that can be executed to reach a partial decision for the applicable rule; pruning the applicable rule to remove executed nodes and generate a pruned rule that includes nodes that cannot be executed; transmitting the pruned rule and partial decision to the first client device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application application is a continuation of U.S. patent application Ser. No. 17/582,998, filed on Jan. 24, 2022, which is a continuation of U.S. patent application Ser. No. 16/803,025, filed on Feb. 27, 2020, which claims the benefit of priority to U.S. Provisional Application Ser. No. 62/954,268, filed on Dec. 27, 2019, each of which are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to messaging systems, and particularly, but not exclusively, to configuring a messaging system to transmit and receive data efficiently.

BACKGROUND

Electronic communications, such as e-mail or text messages, images, video, multimedia, and the like, over networks, such as the Internet, enable the quick communication of data between client devices.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, in accordance with some example embodiments.

FIG. 2 is a diagrammatic representation of a messaging client application, in accordance with some example embodiments.

FIG. 3 is a diagrammatic representation of a data structure as maintained in a database, in accordance with some example embodiments.

FIG. 4 is a diagrammatic representation of a message, in accordance with some example embodiments.

FIG. 5 is a flowchart for an access-limiting process, in accordance with some example embodiments.

FIG. 6 is block diagram showing a software architecture within which the present disclosure may be implemented, in accordance with some example embodiments.

FIG. 7 is a diagrammatic representation of a machine, in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed, in accordance with some example embodiments.

FIG. 8 is a diagrammatic representation of a processing environment, in accordance with some example embodiments.

FIG. 9 is a diagrammatic representation of a client processing environment, in accordance with some example embodiments.

FIGS. 10-13 each illustrate an example rule, in accordance with some example embodiments.

FIG. 14 illustrates a data collection method, in accordance with one example embodiment.

FIG. 15 illustrates a client device configuration method, in accordance with one example embodiment.

DETAILED DESCRIPTION

Example embodiments described herein determine and collect client device capabilities and other data, transform the collected data to a useable format, and then configure client device features (e.g., runtime policy decisions) for efficient data transfer between client devices. Example embodiments deliver the best experience to users given their current device capabilities. Moreover, example embodiments are flexible enough to enable complex optimizations to optimize data transmission based on the shared capabilities between sender and receiver.

Example embodiments make intelligent decisions about when to enable certain features and at what level to enable the certain features. Users have different preferences and capabilities and user devices have different connectivity (e.g., bandwidth) at different times. For example, in one embodiment, a system enables tools tips for a new user and/or allocates upload budget based on the new user's current available bandwidth. Accordingly, tool tips, which would draw on processing power and battery, would be enabled for new users but not experienced users. Further, upload budget for data transmission would be based on the bandwidth available. For example, a lower resolution video can be transmitted to prevent a device from ceasing transmission mid-video when bandwidth is not sufficient to complete transmission under the maximum request duration or would provide a sub-optimal user experience.

FIG. 1 is a block diagram showing an example messaging system 100 for exchanging data (e.g., messages and associated content) over a network. The messaging system 100 includes multiple instances of a client device 102, each of which hosts applications including a messaging client application 104. Each messaging client application 104 is communicatively coupled to other instances of the messaging client application 104 and a messaging server system 108 via a network 106 (e.g., the Internet).

A messaging client application 104 can communicate and exchange data with another messaging client application 104 and with the messaging server system 108 via the network 106. The data exchanged between messaging client application 104, and between a messaging client application 104 and the messaging server system 108, includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality via the network 106 to a particular messaging client application 104. While certain functions of the messaging system 100 are described herein as being performed by either a messaging client application 104 or by the messaging server system 108, the location of certain functionality either within the messaging client application 104 or the messaging server system 108 is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system 108, but to later migrate this technology and functionality to the messaging client application 104 where a client device 102 has a sufficient processing capacity.

The messaging server system 108 supports various services and operations that are provided to the messaging client application 104. Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client application 104. This data may include, message content, client device information, geolocation information, media annotation and overlays, message content persistence conditions, social network information, and live event information, as examples. Data exchanges within the messaging system 100 are invoked and controlled through functions available via user interfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, an Application Program Interface (API) server 110 is coupled to, and provides a programmatic interface to, an application server 112. The application server 112 is communicatively coupled to a database server 118, which facilitates access to a database 120 in which is stored data associated with messages processed by the application server 112.

The Application Program Interface (API) server 110 receives and transmits message data (e.g., commands and message payloads) between the client device 102 and the application server 112. Specifically, the Application Program Interface (API) server 110 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client application 104 in order to invoke functionality of the application server 112. The Application Program Interface (API) server 110 exposes various functions supported by the application server 112, including account registration, login functionality, the sending of messages, via the application server 112, from a particular messaging client application 104 to another messaging client application 104, the sending of media files (e.g., images or video) from a messaging client application 104 to the messaging server application 114, and for possible access by another messaging client application 104, the setting of a collection of media data (e.g., story), the retrieval of a list of friends of a user of a client device 102, the retrieval of such collections, the retrieval of messages and content, the adding and deletion of friends to a social graph, the location of friends within a social graph, and opening an application event (e.g., relating to the messaging client application 104).

The application server 112 hosts applications and subsystems, including a messaging server application 114, an image processing system 116 and a social network system 122. The messaging server application 114 implements message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client application 104. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available, by the messaging server application 114, to the messaging client application 104. Other processor and memory intensive processing of data may also be performed server-side by the messaging server application 114, in view of the hardware requirements for such processing.

The application server 112 also includes an image processing system 116 that is dedicated to performing various image processing operations, typically with respect to images or video received within the payload of a message at the messaging server application 114.

The social network system 122 supports various social networking functions and services, and makes these functions and services available to the messaging server application 114. To this end, the social network system 122 maintains and accesses an entity graph 304 (as shown in FIG. 3 ) within the database 120. Examples of functions and services supported by the social network system 122 include the identification of other users of the messaging system 100 with which a particular user has relationships or is “following,” and also the identification of other entities and interests of a particular user.

The application server 112 is communicatively coupled to a database server 118, which facilitates access to a database 120 in which is stored data associated with messages processed by the messaging server application 114.

FIG. 2 is block diagram illustrating further details regarding the messaging system 100, according to example embodiments. Specifically, the messaging system 100 is shown to comprise the messaging client application 104 and the application server 112, which in turn embody some subsystems, namely an ephemeral timer system 202, a collection management system 204 and an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing the temporary access to content permitted by the messaging client application 104 and the messaging server application 114. To this end, the ephemeral timer system 202 incorporates a number of timers that, based on duration and display parameters associated with a message, or collection of messages (e.g., a story), selectively display and enable access to messages and associated content via the messaging client application 104. Further details regarding the operation of the ephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managing collections of media (e.g., collections of text, image video and audio data). In some examples, a collection of content (e.g., messages, including images, video, text and audio) may be organized into an “event gallery” or an “event story.” Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a “story” for the duration of that music concert. The collection management system 204 may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client application 104.

The collection management system 204 furthermore includes a curation interface 208 that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface 208 enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system 204 employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain embodiments, compensation may be paid to a user for inclusion of user-generated content into a collection. In such cases, the curation interface 208 operates to automatically make payments to such users for the use of their content.

The annotation system 206 provides various functions that enable a user to annotate or otherwise modify or edit media content associated with a message. For example, the annotation system 206 provides functions related to the generation and publishing of media overlays for messages processed by the messaging system 100. The annotation system 206 operatively supplies a media overlay or supplementation (e.g., an image filter) to the messaging client application 104 based on a geolocation of the client device 102. In another example, the annotation system 206 operatively supplies a media overlay to the messaging client application 104 based on other information, such as social network information of the user of the client device 102. A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device 102. For example, the media overlay may include text that can be overlaid on top of a photograph taken by the client device 102. In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, the annotation system 206 uses the geolocation of the client device 102 to identify a media overlay that includes the name of a merchant at the geolocation of the client device 102. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the database 120 and accessed through the database server 118.

In one example embodiment, the annotation system 206 provides a user-based publication platform that enables users to select a geolocation on a map, and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The annotation system 206 generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the annotation system 206 associates the media overlay of a highest bidding merchant with a corresponding geolocation for a predefined amount of time.

FIG. 3 is a schematic diagram illustrating data structures 300 which may be stored in the database 120 of the messaging server system 108, according to certain example embodiments. While the content of the database 120 is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).

The database 120 includes message data stored within a message table 314. The entity table 302 stores entity data, including an entity graph 304. Entities for which records are maintained within the entity table 302 may include individuals, corporate entities, organizations, objects, places, events, etc. Regardless of type, any entity regarding which the messaging server system 108 stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).

The entity graph 304 furthermore stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization) interested-based or activity-based, merely for example.

The database 120 also stores annotation data, in the example form of filters, in an annotation table 312. Filters for which data is stored within the annotation table 312 are associated with and applied to videos (for which data is stored in a video table 310) and/or images (for which data is stored in an image table 308). Filters, in one example, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of varies types, including user-selected filters from a gallery of filters presented to a sending user by the messaging client application 104 when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters) which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the messaging client application 104, based on geolocation information determined by a GPS unit of the client device 102. Another type of filer is a data filer, which may be selectively presented to a sending user by the messaging client application 104, based on other inputs or information gathered by the client device 102 during the message creation process. Example of data filters include current temperature at a specific location, a current speed at which a sending user is traveling, battery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 is so-called “lens” data. A “lens” may be a real-time special effect and sound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in one embodiment, is associated with messages for which records are maintained within the message table 314. Similarly, the image table 308 stores image data associated with messages for which message data is stored in the entity table 302. The entity table 302 may associate various annotations from the annotation table 312 with various images and videos stored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages and associated image, video, or audio data, which are compiled into a collection (e.g., a story or a gallery). The creation of a particular collection may be initiated by a particular user (e.g., each user for which a record is maintained in the entity table 302). A user may create a “personal story” in the form of a collection of content that has been created and sent/broadcast by that user. To this end, the user interface of the messaging client application 104 may include an icon that is user-selectable to enable a sending user to add specific content to his or her personal story.

A collection may also constitute a “live story,” which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a “live story” may constitute a curated stream of user-submitted content from varies locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the messaging client application 104, to contribute content to a particular live story. The live story may be identified to the user by the messaging client application 104, based on his or her location. The end result is a “live story” told from a community perspective.

A further type of content collection is known as a “location story”, which enables a user whose client device 102 is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some embodiments, a contribution to a location story may require a second degree of authentication to verify that the end user belongs to a specific organization or other entity (e.g., is a student on the university campus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400, according to some in some embodiments, generated by a messaging client application 104 for communication to a further messaging client application 104 or the messaging server application 114. The content of a particular message 400 is used to populate the message table 314 stored within the database 120, accessible by the messaging server application 114. Similarly, the content of a message 400 is stored in memory as “in-transit” or “in-flight” data of the client device 102 or the application server 112. The message 400 is shown to include the following components:

-   -   A message identifier 402: a unique identifier that identifies         the message 400.     -   A message text payload 404: text, to be generated by a user via         a user interface of the client device 102 and that is included         in the message 400.     -   A message image payload 406: image data, captured by a camera         component of a client device 102 or retrieved from a memory         component of a client device 102, and that is included in the         message 400.     -   A message video payload 408: video data, captured by a camera         component or retrieved from a memory component of the client         device 102 and that is included in the message 400.     -   A message audio payload 410: audio data, captured by a         microphone or retrieved from a memory component of the client         device 102, and that is included in the message 400.     -   A message annotation 412: annotation data (e.g., image         modifications, stickers or other enhancements) that represents         annotations to be applied to message image payload 406, message         video payload 408, or message audio payload 410 of the message         400.     -   A message duration parameter 414: parameter value indicating, in         seconds, the amount of time for which content of the message         (e.g., the message image payload 406, message video payload 408,         message audio payload 410) is to be presented or made accessible         to a user via the messaging client application 104.     -   A message geolocation parameter 416: geolocation data (e.g.,         latitudinal and longitudinal coordinates) associated with the         content payload of the message. Multiple message geolocation         parameter 416 values may be included in the payload, each of         these parameter values being associated with respect to content         items included in the content (e.g., a specific image into         within the message image payload 406, or a specific video in the         message video payload 408).     -   A message story identifier 418: identifier values identifying         one or more content collections (e.g., “stories”) with which a         particular content item in the message image payload 406 of the         message 400 is associated. For example, multiple images within         the message image payload 406 may each be associated with         multiple content collections using identifier values.     -   A message tag 420: each message 400 may be tagged with multiple         tags, each of which is indicative of the subject matter of         content included in the message payload. For example, where a         particular image included in the message image payload 406         depicts an animal (e.g., a lion), a tag value may be included         within the message tag 420 that is indicative of the relevant         animal. Tag values may be generated manually, based on user         input, or may be automatically generated using, for example,         image recognition.     -   A message sender identifier 422: an identifier (e.g., a         messaging system identifier, email address, or device         identifier) indicative of a user of the client device 102 on         which the message 400 was generated and from which the message         400 was sent     -   A message receiver identifier 424: an identifier (e.g., a         messaging system identifier, email address, or device         identifier) indicative of a user of the client device 102 to         which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload 406 may be a pointer to (or address of) a location within an image table 308. Similarly, values within the message video payload 408 may point to data stored within a video table 310, values stored within the message annotations 412 may point to data stored in an annotation table 312, values stored within the message story identifier 418 may point to data stored in a story table 306, and values stored within the message sender identifier 422 and the message receiver identifier 424 may point to user records stored within an entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process 500, in terms of which access to content (e.g., an ephemeral message 502, and associated multimedia payload of data) or a content collection (e.g., an ephemeral message group 504) may be time-limited (e.g., made ephemeral).

An ephemeral message 502 is shown to be associated with a message duration parameter 506, the value of which determines an amount of time that the ephemeral message 502 will be displayed to a receiving user of the ephemeral message 502 by the messaging client application 104. In one embodiment, an ephemeral message 502 is viewable by a receiving user for up to a maximum of 10 seconds, depending on the amount of time that the sending user specifies using the message duration parameter 506. In an embodiment, the ephemeral message 502 may have an unlimited duration view, e.g., the parameter 506 may be set to unlimited.

The message duration parameter 506 and the message receiver identifier 424 are shown to be inputs to a message timer 512, which is responsible for determining the amount of time that the ephemeral message 502 is shown to a particular receiving user identified by the message receiver identifier 424. In particular, the ephemeral message 502 will only be shown to the relevant receiving user for a time period determined by the value of the message duration parameter 506. The message timer 512 is shown to provide output to a more generalized ephemeral timer system 202, which is responsible for the overall timing of display of content (e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within an ephemeral message group 504 (e.g., a collection of messages in a personal story, or an event story). The ephemeral message group 504 has an associated group duration parameter 508, a value of which determines a time-duration for which the ephemeral message group 504 is presented and accessible to users of the messaging system 100. The group duration parameter 508, for example, may be the duration of a music concert, where the ephemeral message group 504 is a collection of content pertaining to that concert. Alternatively, a user (either the owning user or a curator user) may specify the value for the group duration parameter 508 when performing the setup and creation of the ephemeral message group 504.

Additionally, each ephemeral message 502 within the ephemeral message group 504 has an associated group participation parameter 510, a value of which determines the duration of time for which the ephemeral message 502 will be accessible within the context of the ephemeral message group 504. Accordingly, a particular ephemeral message group 504 may “expire” and become inaccessible within the context of the ephemeral message group 504, prior to the ephemeral message group 504 itself expiring in terms of the group duration parameter 508. The group duration parameter 508, group participation parameter 510, and message receiver identifier 424 each provide input to a group timer 514, which operationally determines, firstly, whether a particular ephemeral message 502 of the ephemeral message group 504 will be displayed to a particular receiving user and, if so, for how long. Note that the ephemeral message group 504 is also aware of the identity of the particular receiving user as a result of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overall lifespan of an associated ephemeral message group 504, as well as an individual ephemeral message 502 included in the ephemeral message group 504. In one embodiment, each and every ephemeral message 502 within the ephemeral message group 504 remains viewable and accessible for a time-period specified by the group duration parameter 508. In a further embodiment, a certain ephemeral message 502 may expire, within the context of ephemeral message group 504, based on a group participation parameter 510. Note that a message duration parameter 506 may still determine the duration of time for which a particular ephemeral message 502 is displayed to a receiving user, even within the context of the ephemeral message group 504. Accordingly, the message duration parameter 506 determines the duration of time that a particular ephemeral message 502 is displayed to a receiving user, regardless of whether the receiving user is viewing that ephemeral message 502 inside or outside the context of an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove a particular ephemeral message 502 from the ephemeral message group 504 based on a determination that it has exceeded an associated group participation parameter 510. For example, when a sending user has established a group participation parameter 510 of 24 hours from posting, the ephemeral timer system 202 will remove the relevant ephemeral message 502 from the ephemeral message group 504 after the specified 24 hours. The ephemeral timer system 202 also operates to remove an ephemeral message group 504 either when the group participation parameter 510 for each and every ephemeral message 502 within the ephemeral message group 504 has expired, or when the ephemeral message group 504 itself has expired in terms of the group duration parameter 508.

In certain use cases, a creator of a particular ephemeral message group 504 may specify an indefinite group duration parameter 508. In this case, the expiration of the group participation parameter 510 for the last remaining ephemeral message 502 within the ephemeral message group 504 will determine when the ephemeral message group 504 itself expires. In this case, a new ephemeral message 502, added to the ephemeral message group 504, with a new group participation parameter 510, effectively extends the life of an ephemeral message group 504 to equal the value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that an ephemeral message group 504 has expired (e.g., is no longer accessible), the ephemeral timer system 202 communicates with the messaging system 100 (and, for example, specifically the messaging client application 104) to cause an indicium (e.g., an icon) associated with the relevant ephemeral message group 504 to no longer be displayed within a user interface of the messaging client application 104. Similarly, when the ephemeral timer system 202 determines that the message duration parameter 506 for a particular ephemeral message 502 has expired, the ephemeral timer system 202 causes the messaging client application 104 to no longer display an indicium (e.g., an icon or textual identification) associated with the ephemeral message 502.

FIG. 6 is a block diagram 600 illustrating a software architecture 604, which can be installed on any one or more of the devices described herein. The software architecture 604 is supported by hardware such as a machine 602 that includes processors 620, memory 626, and I/O components 638. In this example, the software architecture 604 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 604 includes layers such as an operating system 612, libraries 610, frameworks 608, and applications 606. Operationally, the applications 606 invoke API calls 650 through the software stack and receive messages 652 in response to the API calls 650.

The operating system 612 manages hardware resources and provides common services. The operating system 612 includes, for example, a kernel 614, services 616, and drivers 622. The kernel 614 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 614 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 616 can provide other common services for the other software layers. The drivers 622 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 622 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries 610 provide a low-level common infrastructure used by the applications 606. The libraries 610 can include system libraries 618 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 610 can include API libraries 624 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 610 can also include a wide variety of other libraries 628 to provide many other APIs to the applications 606.

The frameworks 608 provide a high-level common infrastructure that is used by the applications 606. For example, the frameworks 608 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 608 can provide a broad spectrum of other APIs that can be used by the applications 606, some of which may be specific to a particular operating system or platform.

In an example embodiment, the applications 606 may include a home application 636, a contacts application 630, a browser application 632, a book reader application 634, a location application 642, a media application 644, a messaging application 646, a game application 648, and a broad assortment of other applications such as third-party applications 640. The applications 606 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 606, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party applications 640 (e.g., applications developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party applications 640 can invoke the API calls 650 provided by the operating system 612 to facilitate functionality described herein.

FIG. 7 is a diagrammatic representation of a machine 700 within which instructions 708 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 700 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 708 may cause the machine 700 to execute any one or more of the methods described herein. The instructions 708 transform the general, non-programmed machine 700 into a particular machine 700 programmed to carry out the described and illustrated functions in the manner described. The machine 700 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 700 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 700 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 708, sequentially or otherwise, that specify actions to be taken by the machine 700. Further, while only a single machine 700 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 708 to perform any one or more of the methodologies discussed herein.

The machine 700 may include processors 702, memory 704, and I/O components 742, which may be configured to communicate with each other via a bus 744. In an example embodiment, the processors 702 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 706 and a processor 710 that execute the instructions 708. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 7 shows multiple processors 702, the machine 700 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory 704 includes a main memory 712, a static memory 714, and a storage unit 716, both accessible to the processors 702 via the bus 744. The main memory 704, the static memory 714, and storage unit 716 store the instructions 708 embodying any one or more of the methodologies or functions described herein. The instructions 708 may also reside, completely or partially, within the main memory 712, within the static memory 714, within machine-readable medium 718 within the storage unit 716, within at least one of the processors 702 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 700.

The I/O components 742 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 742 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 742 may include many other components that are not shown in FIG. 7 . In various example embodiments, the I/O components 742 may include output components 728 and input components 730. The output components 728 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 730 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further example embodiments, the I/O components 742 may include biometric components 732, motion components 734, environmental components 736, or position components 738, among a wide array of other components. For example, the biometric components 732 include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 734 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 736 include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 738 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components 742 further include communication components 740 operable to couple the machine 700 to a network 720 or devices 722 via a coupling 724 and a coupling 726, respectively. For example, the communication components 740 may include a network interface component or another suitable device to interface with the network 720. In further examples, the communication components 740 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 722 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 740 may detect identifiers or include components operable to detect identifiers. For example, the communication components 740 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 740, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

The various memories (e.g., memory 704, main memory 712, static memory 714, and/or memory of the processors 702) and/or storage unit 716 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 708), when executed by processors 702, cause various operations to implement the disclosed embodiments.

The instructions 708 may be transmitted or received over the network 720, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 740) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 708 may be transmitted or received using a transmission medium via the coupling 726 (e.g., a peer-to-peer coupling) to the devices 722.

Turning now to FIG. 8 , there is shown a diagrammatic representation of a processing environment 800, which includes a processor 802 (e.g., a GPU, CPU or combination thereof).

The processor 802 is shown as coupled with a power source 804, and to include (either permanently configured or temporarily instantiated) modules, namely a circumstance engine 810, a collection service 812, a device lookup Service 814, rules 816, a data bank 818 and a data processing engine 820. The circumstance engine 810 makes decisions at runtime on a client device 102 based on the device's current circumstances. Decisions can include, for example, whether to enable (grant access to) a feature, and whether to prefetch video content for a particular user given the device and other circumstances. Decisions are based on rules stored in rules 816. A value derived from the decisions could be a Boolean, scalar or more complex constructs. Rules 816 comprises rules that represent value and decision criteria and can be in Boolean format and is discussed further below in more detail. Each decision may have multiple rules tied together with a rule ID. Rules could include, for example, enabling tools tips for a new user or allocating upload budget based on the new user's country, prior upload history, and current bandwidth available, and so forth. The circumstance engine 810 evaluates rules 816 using data from the data bank 818 to determine how to reach a decision to grant access when a client feature 910 requests access. Once a decision is reached, the circumstance engine 810 can configure the client feature 910 (of FIG. 9 ) accordingly.

The collection service 812 is responsible for gathering information from client devices. This information includes device capabilities (e.g., hardware HEVC support) and benchmark results. The collection service 812 can throttle task frequency and background data collection to prevent excess battery or network usage on common devices, and collect more samples from rarer devices when needed. The circumstance engine 810 can use benchmark results dynamically, including notifying a client device that other client device capabilities have changed (e.g., switch devices, change OS version, change an application version (app_version), and/or move network types). The collection service 812, in general, only requests benchmarks on a device when the data bank 818 lacks information about that device. For example, the collection service 812 does not need to poll every ‘SM-G950U’ (S8) to find out if it can hardware encode HEVC or H.265. Additionally, there are some benchmarks that are more important and thus should be run first. Accordingly, the device lookup service 814 will determine what information is missing for a given device and ask the collection service 812 to retrieve that information from a relevant client device.

The data processing engine 820 is responsible for storing data collected by the collection service 812 and transforming it to a useful format for the device lookup service 814. That is, when the circumstance engine 810 executes a rule, the circumstance engine 810 can call the device lookup service 814 to pull data to make a decision. The circumstance engine 810 can then inform a corresponding circumstance engine 912 on the client device 102 to perform the decision. If the circumstance engine 810 does not have all data needed to execute a rule, the circumstance engine 810 can prune the rule and transmit a partial decision and pruned rule to the circumstance engine 912 to complete evaluating the rule and then implement a final decision to configure the client feature 910 of FIG. 9 .

FIG. 9 is a diagrammatic representation of a client processing environment, in accordance with some example embodiments. The processor 902 includes one or more client features 910, the circumstance engine 912, a collection scheduler 914, rules 916 and a data bank 918. At runtime, the client feature 910 will call on the circumstance engine 912 to make a decision. The circumstance engine 912 can execute one or more rules from the rules 916 locally and/or call the circumstance engine 810 to at least partially execute a relevant rule(s). The circumstance engine 912 can make a decision using data in the data bank 918, which includes hardware device capabilities and/or user data (e.g., data usage, number of other client devices regularly in contact with, network type, connection type, storage memory remaining). Once a decision is reached, either by the circumstance engine 912 and/or in combination with the circumstance engine 810, the client feature 910 then acts per the decision made (e.g., implements the configuration decided on). The collection scheduler 914 schedules the collection of device data for the collection service 812 to ensure minimal interruption of the client device 102 (e.g., usage of processing power, battery, bandwidth) when the client device 102 is in use (e.g., collect data at night when a user is not communicating using the device).

The collection scheduler 914 can provide some device properties: operating system (OS) version, app or application version, device model, build flavor, and so forth to the collection service 812. Collection scheduler 914 could also pass other dynamic info like current battery level, free memory and disk level as request parameters.

In addition, collection scheduler 914 can provide higher level building blocks like Microbenchmarks, which can be a small program that can run in for less than one second and provide information about the performance of a client device 102 Component. The data processing engine 820 generates a mapping from device models to the aggregated Microbenchmark results and stores the mapping as a daily snapshot on data bank 818.

FIG. 10 illustrates an example rule 1000, in accordance with one example embodiment. Rules are based on properties like “device supports HEVC hardware encoding”, “user is heavy story poster”, and “estimated available upload bandwidth”. The circumstance engine 810 and/or circumstance engine 912 execute the example rule 1000 to reach a decision. The example rule 1000 can control an upload size for a file or related group of files. A default value for the upload size may be 2.5 MB but can be adjusted upwards (e.g., to 4 MB) based on the execution of the example rule 1000.

In example rule 1000, a decision enables a larger upload if a client device is using App Version>10.30 1006, if Bandwidth>1 MPBS 1008 and (the Is Power User 1010 (e.g., influencer) or Build Flavor is Beta 1012).

FIG. 11 illustrates an example rule 1100, in accordance with one example embodiment. The example rule 1100 can control an upload size for a file or related group of files. In 1100, the upload size can be increased from 2.5 MB to, for example, 5 MB. Example rule 1100 requires an App Version greater than 10.30 1004, that User is Employee 1106, and that Bandwidth is greater than 2 MBPS 1108.

FIG. 12 illustrates an example rule 1200, in accordance with one example embodiment. In example rule 1200, bandwidth can be reduced from a default of 2.5 MB to, for example, 2 MB, if the App Version is greater than 10.30 1204, the Location is Brazil 1206, and the Bandwidth is less than 512 KBPS 1208. Accordingly, example rule 1200 is location dependent. That is, the location of the Client Device effects execution of the example rule 1200.

The example rules 1000, 1100 and 1200, are each initially evaluated on a server system (e.g., e.g., messaging server system 108 by circumstance engine 810) and partial matches are sent to the client device 102 (e.g., to circumstance engine 912) to be evaluated when the feature requests the decision. For example, in the above example rules, the circumstance engine 810 evaluates all nodes except for available bandwidth. Accordingly, the circumstance engine 810 will evaluate the nodes of the example rules and return partial matches to the circumstance engine 912 and a pruned rule including available bandwidth to complete evaluation of the rule.

FIG. 13 illustrates an example rule 1300, in accordance with one example embodiment. Example rule 1300 is a rule for prefetching video content in the messaging system 100. Example rule 1300 comprises a Bandwidth greater than 3 1306 or a Heavy Discover User 1308, a Battery Level greater than 70 1310 and a Device Cluster greater than 6 1312. The device cluster of a device is a composite score based on metrics (e.g., 10 metrics) of the messaging client application 104. In an embodiment, the device cluster can also take into account hardware, such as availability of additional computing devices, such as graphics processing units (GPUs) and digital signal processors (DSPs). If all conditions are met, then data is prefetched.

In one embodiment, nodes may be prioritized. For example, to prefetch data, a priority node could include available memory to store the prefetched data. If memory is limited, other nodes in the rule do not need to be traversed (such as battery level, connection type, and/or bandwidth). Other example rules include prefetching based on nodes for a client device app version, client device location, and bandwidth or upload file size with nodes including bandwidth and connection type.

As discussed previously in conjunction with FIG. 8 and FIGS. 10-13 above, FIG. 14 illustrates a data collection method 1400, in accordance with one example embodiment. First, device lookup Service 814 determines needed data (1402) from client devices and informs the collection scheduler 914 accordingly. For example, the device look up Service 814 determines data missing from the data bank 818 for a given device, such as hardware HEVC support or hardware encode HEVC for a specific mobile device type. The collection scheduler 914 schedules a time to collect data when the device is likely not to be in use (to avoid interruption of processing on the device) and collects client data (1404) and transmits it to the collection service 812. Collection is done by calling relevant APIs. For example, in Android, to determine sensors available, the collection service 812 can call a getSensorList API. In another example, MediaCodecInfo.VideoCapabilities.getSupportedPerformancePoints( ) API returns a codec's ability to render video at a specific height, width and frame rate. The collection service 812 receives the client data (1406) from the relevant API or other benchmark mechanism and transmits the client data to the data processing engine 820. The data processing engine 820 then publishes the client data to the data bank 818 in a format that can be read by the device lookup service 814 and the circumstance engine 810. In an embodiment, the collecting (1404) can be from a first client device and a plurality of other client devices that communicate with the first client device.

As discussed previously in conjunction FIG. 9 and FIGS. 10-13 above, FIG. 15 illustrates a client device configuration method 1500, in accordance with one example embodiment. In an embodiment, the method 1500 is executed after the method 1400 is executed. First, a client feature 910 requests access (permission) (1502) to use a client device feature from the circumstance engine 912. A client feature might include, for example, prefetching data or setting maximum file size for uploading data. It is to be understood that the client features and rules are not limited to these examples and that any decision affecting execution of a client feature (e.g., encoding format, encryption) can be implemented.

The circumstance engine 912 on the client device sends the request to access the client device feature to the circumstance engine 810 (1504). The circumstance engine 810 receives the request to access the client feature and optionally determines the applicable rule for the access request (1506) from the rules 816. In one embodiment, the applicable rule has a plurality of nodes. For example, rules 1000, 1100, 1200, and 1300 each have a plurality of nodes, as described above. Each feature has a rule associated with that feature as stored in the rules 816 in a table or other format. The rules 816 are not limited to the rules 1000-1300 but include any number of rules including, but not limited to, the example properties listed below in any combination or subset thereof. The device lookup service 814 then looks up the type of device sending the request (1508) and requests device capabilities from the device and/or data bank 818/918. The circumstance engine 810 determines nodes that can be executed and that cannot be executed based on device capabilities received and executes the rule based on the received device capabilities data from the Device Lookup Service 814 (e.g., type of device) (1510). For information unavailable to the circumstance engine 810, the circumstance engine 810 prunes the rule (1512) and sends a partial decision and pruned rule (undetermined nodes) to the circumstance engine 912 (1514) to use local data (e.g., bandwidth) to complete the decision. For example, in the rule 1100, the circumstance engine 810 executes the node 1106 to generate a partial decision and prunes the rule 1100 to remove the executed node 1106 to generate a pruned rule including any nodes that cannot be executed. The circumstance engine 810 then sends the result of node 1106 as the partial decision and a pruned rule 1100 that contains nodes 1104 and 1108, which can be determined by the circumstance engine 912 on the device. The circumstance engine 912 on the client device then runs (1516) the pruned rule based on the partial decision and local data and provides access to the client feature. The client feature 910 then configures and executes the feature based on the final decision reached.

While method 1500 is shown as executed in combination between circumstance engine 810 and circumstance engine 912, it is possible for either one to execute the method 1500 without the other. Further, in an embodiment, the method 1500 can be executed asynchronously. For example, the sending (1504) can occur during app startup and is cached in a local database. Accordingly, when a client feature requests (1502) access, the method 1500 proceeds to the client running (1516) the pruned rule.

In an embodiment, the method 1500 can execute a rule based on data from multiple devices. For example, a rule might have a node that determines an encoding codec to use based on a codec available on multiple devices (common to multiple devices) or other features common to multiple devices that previously communicated with each other. As mentioned earlier, the rule can also contain priority and non-priority nodes such that priority nodes are executed before non-priority nodes.

Accordingly, decisions are based on rules that evaluate user and/or device properties. Rules can operate on properties that will be evaluated in real-time or near real-time by the clients consuming the configuration or properties that are stored and evaluated on the server. Properties like ‘available_bandwidth’ and ‘battery_level’ are examples of real-time properties that can be evaluated by the client whereas properties like ‘is_heavy_discover_user’ and ‘is_new_user’ are examples of properties that are computed and evaluated on the server.

Other rules can include turning off transcoding if a client device has historically stable uploads, streaming instead of prefetching if a current connection is WiFi, changing upload media format based on recipients decoding capabilities, and so forth. Rules can also be based on the following properties to form nodes:

-   -   DEVICE_MODELCOUNTRY     -   operating system (OS)     -   operating system version     -   application version     -   build flavor (version)     -   user identifier LOCATION of the device     -   IS DEVICE CHARGING?     -   battery level     -   DEVICE CLUSTER     -   IS_OFFLINE?     -   BANDWIDTH     -   DEVICE_BRAND     -   IS_EMPLOYEE?     -   USERNAME     -   IS TEST USER?     -   USER PROFILE     -   SCREEN WIDTH     -   SCREEN HEIGHT     -   HEVC SUPPORT     -   DISK SIZE     -   DISK AVAILABLE     -   NETWORK TYPE     -   MAX VIDEO WIDTH     -   MAX VIDEO HEIGHT     -   MEDIA TYPE     -   IS PUBLIC STORY?     -   IS OFFICIAL STORY?     -   IS NETWORK METERED?     -   IS DEVICE ROAMING?     -   APPLICATION ENGAGEMENT LEVEL (frequency of use)     -   COMMUNICATION ENGAGEMENT LEVEL (frequency of message         transmission)     -   FRIEND STORY ENGAGEMENT LEVEL     -   PUBLIC USER STORY ENGAGEMENT LEVEL     -   PUBLISHER STORY ENGAGEMENT LEVEL     -   LENS ENGAGEMENT LEVEL (frequency of augmented reality usage)     -   CAMERA CONTEXT     -   CAMERA DIRECTION     -   CAMERA FLASH_STATE     -   CAMERA API     -   NON FRIEND STORY ENGAGEMENT LEVEL     -   FOLLOWER SIZE LEVEL     -   LEGACY MUSHROOM CONTENT TYPE

In an embodiment, the collection service 812 is responsible for gathering information from client devices. This information can also include device processing capabilities and other hardware (e.g., GPU, DSP, etc.) and benchmark results.

In an embodiment, at runtime, the client feature 910 will call on the circumstance engine 912 to make a decision. The circumstance engine 912 can execute one or more rules from the rules 916 locally and/or call the circumstance engine 810 to at least partially execute a relevant rule(s). The circumstance engine 912 can make a decision using data in the data bank 918, which includes device hardware, capabilities and/or user data (e.g., GPU processing power, DSP processing power, data usage, number of other client devices regularly in contact with, network type, connection type, storage memory remaining).

In an embodiment, the collection scheduler 914 can provide some device properties: operating system (OS) version, app or application version, device model, build flavor, DSP and GPU processing power, if any, and so forth to the collection service 812.

In an embodiment, the method 1500 is executed after the method 1400 is executed. First, a client feature 910 requests access (permission) (1502) to use a client device feature from the circumstance engine 912. A client feature might include, for example, computer-vision related features, such as augmented reality.

More specifically, frame processing is one of the major stages in the augmented reality processing pipeline. Frame processing frames per second (FPS) has a direct impact on user engagement. As the FPS reduces, the user engagement metrics drop significantly. Therefore, embodiments provide adaptive frame processing to maintain a reasonable processing FPS across device categories based on availability of a GPU or DSP. Accordingly, an embodiment provides video frames of different resolutions. Higher image resolution is used for higher end devices (e.g., GPU rendering), and the lower image resolution is used for lower end devices not having a GPU or a less powerful GPU (e.g., based on device cluster score). Alternatively, resources can be optimized by lowering high resolution images and textures to maintain an FPS.

In another example, input size of deep neural networks (DNN) can be adjusted based on hardware available (e.g., device cluster score). Deep neural networks have become more important for computer vision features. DNN-based features include, but are not limited to, image semantic segmentation and object detection. To make sure the DNN models can run smoothly on major device categories, embodiments adjust the computation workloads based on the device cluster score. For example, for human body segmentation, embodiments can change the size of the model input to achieve reasonable FPS on major device capabilities (e.g., device cluster). That is, device dependent optimization based on hardware capabilities. Other hardware that could adjust cluster device score and therefore affect client features include cryptographic accelerators for encryption, AI accelerators for artificial intelligence applications, especially machine vision, image compression accelerators, etc.

An example rule for augmented reality or other video feature: if device cluster score is low, e.g., GPU clock speed is low or device lacks a GPU, maintain frames per second and reduce resolution. Alternatively, if a GPU is present, offload computation to GPU so FPS can be maintained without lowering resolution.

An example rule for neural networks for use in computer vision: if device cluster score is low, e.g., processor clock speed is low or device lack a co-processor, maintain frames per second and reduce size of model input for human body segmentation (to decrease computational workload). Alternatively, if a co-processor is present, offload computation to a co-processor (GPU or AI accelerator) so FPS and model input size can be maintained. Alternatively, if neural net hardware acceleration exists, maintain frames per second and maintain or increase model input for human body segmentation or other computations.

Rules can include a combination of hardware related nodes, such as GPU capability, and real-time circumstances nodes, such as battery level. For example, even if a co-processor is present, if battery level is low (less than a predetermined amount) than a co-processor will not be engaged and instead resolution will be lowered to maintain FPS. That is, the battery level is a priority node while co-processor availability is not.

“Signal Medium” refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure.

“Communication Network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.

“Processor” refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., “commands”, “op codes”, “machine code”, etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC) or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously.

“Machine-Storage Medium” refers to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions, routines and/or data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.”

“Component” refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component”(or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In embodiments in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors 1004 or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented components may be distributed across a number of geographic locations.

“Carrier Signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.

“Computer-Readable Medium” refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure.

“Client Device” refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may use to access a network.

“Ephemeral Message” refers to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory.

The following examples describe various embodiments of methods, machine-readable media, and systems (e.g., machines, devices, or other apparatus) discussed herein.

1. A computer-implemented method of enabling a client feature, comprising:

-   -   receiving, at a server from a first client device, a request to         access a client feature on the first client device;     -   determining, by the server, an applicable rule for the access         request, the applicable rule having a plurality of nodes;     -   determining, by the server, device capabilities needed for the         determined rule;     -   determining, by the server, nodes that can be executed and nodes         that cannot be executed, based on the device capabilities, the         nodes that can be executed including device hardware         capabilities and the nodes that cannot be executed including         real-time device capabilities;     -   executing, by the server, the nodes that can be executed to         reach a partial decision for the applicable rule;     -   pruning the applicable rule to remove executed nodes and         generate a pruned rule that includes the nodes that cannot be         executed; and     -   transmitting the pruned rule and the partial decision to the         first client device, the pruned rule being executed on the first         client device with the partial decision to generate a final         decision,     -   the client feature being configured based on the final decision.

2. The method of example 1, further comprising collecting device capabilities from the first client device and a plurality of other client devices that communicated with the first client device.

3. The method of example 2, wherein the applicable rule determines an image resolution in an augmented reality application for the first client device.

4. The method of example 2, further comprising scheduling the collecting to avoid interruption of processing on the devices.

5. The method of example 1, wherein the applicable rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.

6. The method of example 5, wherein the applicable rule determines that an image resolution is to be reduced and a priority node includes battery level and a non-priority node includes co-processor availability.

7. The method of example 1, wherein the applicable rule determines model input size in a deep neural network and a node includes co-processor availability.

8. The method of example 7, wherein the deep neural network performs human body segmentation and maintains a frames per second level for the client feature.

9. The method of example 1, wherein the applicable rule determines texture level and maintains a frames per second level for the client feature.

10. A computer-readable medium storing instructions that, when executed by one or more computer processors of a server, cause the server to perform operations comprising:

-   -   receiving, at the server from a first client device, a request         to access a client feature on the first client device;     -   determining, by the server, an applicable rule for the access         request, the applicable rule having a plurality of nodes;     -   determining, by the server, device capabilities needed for the         determined rule;     -   determining, by the server, nodes that can be executed and nodes         that cannot be executed, based on the device capabilities, the         nodes that can be executed including device hardware         capabilities and the nodes that cannot be executed including         real-time device capabilities;     -   executing, by the server, the nodes that can be executed to         reach a partial decision for the applicable rule;     -   pruning the applicable rule to remove executed nodes and         generate a pruned rule that includes the nodes that cannot be         executed; and     -   transmitting the pruned rule and the partial decision to the         first client device, the pruned rule being executed on the first         client device with the partial decision to generate a final         decision,     -   the client feature being configured based on the final decision.

11. A messaging system, comprising:

-   -   a memory that stores instructions; and     -   one or more processors configured by the instructions to perform         operations comprising:     -   receiving, at the one or more processors from a first client         device, a request to access a client feature on the first client         device;     -   determining, by the one or more processors, an applicable rule         for the access request, the applicable rule having a plurality         of nodes;     -   determining, by the one or more processors, device capabilities         needed for the determined rule;     -   determining, by the one or more processors, nodes that can be         executed and nodes that cannot be executed, based on the device         capabilities, the nodes that can be executed including device         hardware capabilities and the nodes that cannot be executed         including real-time device capabilities;     -   executing, by the one or more processors, the nodes that can be         executed to reach a partial decision for the applicable rule;     -   pruning the applicable rule to remove executed nodes and         generate a pruned rule that includes the nodes that cannot be         executed; and     -   transmitting the pruned rule and the partial decision to the         first client device, the pruned rule being executed on the first         client device with the partial decision to generate a final         decision,     -   the client feature being configured based on the final decision.

12. The messaging system of example 11, wherein the operations further comprise collecting device capabilities from the first client device and a plurality of other client devices that communicated with the first client device.

13. The messaging system of example 12, wherein the applicable rule determines an image resolution in an augmented reality application for the first client device

14. The messaging system of example 12, wherein the operations further comprise scheduling the collecting to avoid interruption of processing on the devices.

15. The messaging system of example 11, wherein the applicable rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.

16. The messaging system of example 15, wherein the applicable rule determines that an image resolution is to be reduced and a priority node includes battery level and a non-priority node includes co-processor availability.

17. The messaging system of example 11, wherein the applicable rule determines model input size in a deep neural network and a node includes co-processor availability.

18. The messaging system of example 17, wherein the deep neural network performs human body segmentation and maintains a frames per second level for the client feature.

19. The messaging system of example 11, wherein the applicable rule determines texture level and maintains a frames per second level for the client feature.

20. A messaging system, comprising:

-   -   a memory that stores instructions; and     -   one or more processors configured by the instructions to perform         operations comprising:     -   receiving, at the one or more processors from a first client         device, a request to access a client feature on the first client         device;     -   determining, by the one or more processors, an applicable rule         for the access request, the applicable rule having a plurality         of nodes;     -   determining, by the one or more processors, device capabilities         needed for the determined rule;     -   executing, by the one or more processors, the plurality of nodes         to reach a decision for the applicable rule; and     -   transmitting the decision to the first client device, the client         feature being configured based on the decision. 

What is claimed is:
 1. A computer-implemented method of enabling a client feature, comprising: receiving, by at least one processor, a request to access a client feature on a first client device; determining, by the at least one processor, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the at least one processor, nodes that can be executed, the nodes that can be executed including device hardware capabilities and real-time device capabilities; executing, by the at least one processor, the nodes that can be executed to reach a decision for the applicable rule; configuring, by the at least one processor, the requested client feature based on the decision.
 2. The method of claim 1, further comprising collecting device capabilities from a plurality of other client devices that communicated with the first client device.
 3. The method of claim 2, wherein the applicable rule determines an image resolution in an augmented reality application for the first client device.
 4. The method of claim 1, further comprising pruning the determined applicable rule to remove nodes that cannot be executed due to lack of data.
 5. The method of claim 1, wherein the applicable rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.
 6. The method of claim 5, wherein the applicable rule determines that an image resolution is to be reduced and a priority node includes battery level and a non-priority node includes co-processor availability.
 7. The method of claim 1, wherein the applicable rule determines model input size in a deep neural network and a node includes co-processor availability.
 8. The method of claim 7, wherein the deep neural network performs human body segmentation and maintains a frames per second level for the client feature.
 9. The method of claim 1, wherein the applicable rule determines texture level and maintains a frames per second level for the client feature.
 10. A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors, cause one or more of the one or more computer processors to perform operations comprising: receiving a request to access a client feature on a first client device; determining an applicable rule for the access request, the applicable rule having a plurality of nodes; determining nodes that can be executed, the nodes that can be executed including device hardware capabilities and real-time device capabilities; executing the nodes that can be executed to reach a decision for the applicable rule; configuring the requested client feature based on the decision.
 11. A first client device, comprising: a memory that stores instructions; and one or more processors configured by the instructions to perform operations comprising: receiving a request to access a client feature on a first client device; determining an applicable rule for the access request, the applicable rule having a plurality of nodes; determining nodes that can be executed, the nodes that can be executed including device hardware capabilities and real-time device capabilities; executing nodes that can be executed to reach a decision for the applicable rule; configuring the requested client feature based on the decision.
 12. The first client device of claim 11, wherein the operations further comprise collecting device capabilities from the first client device and a plurality of other client devices that communicated with the first client device.
 13. The first client device of claim 12, wherein the applicable rule determines an image resolution in an augmented reality application for the first client device.
 14. The first client device of claim 12, wherein the operations further comprise pruning the determined applicable rule to remove nodes that cannot be executed due to lack of data.
 15. The first client device of claim 11, wherein the applicable rule comprises priority nodes and non-priority nodes and the priority nodes are executed before the non-priority nodes.
 16. The first client device of claim 15, wherein the applicable rule determines that an image resolution is to be reduced and a priority node includes battery level and a non-priority node includes co-processor availability.
 17. The first client device of claim 11, wherein the applicable rule determines model input size in a deep neural network and a node includes co-processor availability.
 18. The first client device of claim 17, wherein the deep neural network performs human body segmentation and maintains a frames per second level for the client feature.
 19. The first client device of claim 11, wherein the applicable rule determines texture level and maintains a frames per second level for the client feature. 